Use of user attributes in recommender systems dates at least back to demographic recommender systems [1]}, as previously mentioned.
Here, we briefly cover some examples of more recent collaborative filtering systems that have integrated user side information.
Variational Autoencoder approaches include [2]}, which stacks denoising auto-encoders (SDAE) to integrate side information into the latent factors, and [3]}, which uses a collective Variational Autoencoder (cVAE) for integrating side information for Top-N recommendation.
More recent work includes a clustering-based collaborative filtering algorithm that integrates user side information (such as age, gender and occupation) in a deep neural network [4]} and a Gaussian process based recommendation framework that leverages side information [5]}.
These approaches illustrate that researchers are interested in user attributes not just for improving cold start and sparsity, but also recommender performance across users.
For further examples of recent work, see [6]}, [7]}.
